AbstractObjective: The purpose of the study was to investigate the theoretical foundations of hypothesis testing and to look into its rational and philosophical concepts and practical implications of this scientific process in generating new knowledge.Methodology: To this end, the existing literature ...
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AbstractObjective: The purpose of the study was to investigate the theoretical foundations of hypothesis testing and to look into its rational and philosophical concepts and practical implications of this scientific process in generating new knowledge.Methodology: To this end, the existing literature and leading references on the scientific bases, procedures, and statistical methods in relation to hypothesis testing in order to determine cause and effect relationship or the correlation between the variables were purposefuly studied. The rational, philosophical, and practical considerations and inferences which are presented in the present article are the result of the author’s nearly forty years of experience and teaching in these domains.Results: The results of the studies indicated that almost all the statistical inferences encompass three kinds of errors: Type I, Type II, and other types of errors that researchers may make during the process of their studies (Standard Deviation). The inferential statistics results are valuable when they have least amount of mistakes and errors. To achive this goal, the Type I error can be controlled by minimizing the probability of its occurrence by the researcher. Type II error isdecreased by undertaking provisions like: adding the intensity of independent variable, increasing the number of the participants, choosing a higher degree of probability for Type I error, and using parametric statistical methods instead of nonparametric ones. Ultimately, there is a considerable number of statistical and research method techniques for lowering the indices of variability and deviations of the scores. Interestingly enough, there is a negative relationship between Type I and Type II error. In other words, lowering the probability of one of them will have an incremental effect on the other. From a practical standpoint, Type I error recommends inefficient and unproductive knowledge to the target population; whereas, Type II error may deprive people from useful and effective information.Conclusion: Researchers’ mistakes may absolutely create double-sided damages to society. The researcher’s choice between the first two types of errors relies on the significance of the application of the results.Key Words: Statistical inference, Power of the test, Types of error, Standard deviation, Standard error.
